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Metabolomics on vascular events and death after acute ischemic stroke: A prospective matched nested case-control
Chaofu Ke1, Mengyao Shi1, Daoxia Guo2
1Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, 215123, PR China.
Background And Aims:
Ischemic stroke is a major contributor to global mortality and disability. Metabolomics represents a powerful tool for discovering biomarkers of ischemic stroke due to its ability to detect metabolites small enough to cross the blood-brain barrier. The aim of this study is to identify potential metabolic biomarkers for predicting 1-year vascular events and death after acute ischemic stroke (AIS).
Methods:
We adopted a nested case-control design from a multicenter prospective cohort study. A total of 143 AIS patients with 1-year vascular events/death and 143 sex-, age- and center-matched patients without 1-year vascular events/death were selected, and further divided into the discovery set (n = 140) and validation set (n = 146). We then performed untargeted metabolomics analysis by liquid chromatography coupled to mass spectrometry.
Results:
Metabolomics analyses could predict 1-year vascular events and death after AIS using pattern recognition methods. Eight metabolites (e.g., LysoPC(18:1)) were identified as potential biomarkers of AIS prognosis. Four of them (e.g., PS(O-18:0/0:0)) were found to be significantly decreased in patients with early vascular events/death. Adding these metabolic biomarkers to traditional factors resulted in a great improvement of the predictive utility for 1-year vascular events/death, with net reclassification index and integrated discrimination improvement being 0.9143 (p < 0.0001) and 0.1906 (p < 0.0001) in the discovery cohort, and 0.9041 (p < 0.0001) and 0.1896 (p < 0.0001) in the validation cohort.
Conclusions:
This study found several metabolic biomarkers for 1-year vascular events and death after AIS, providing opportunities for the construction of prognostic models and the discovery of novel therapeutic targets.
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